Information overload is a well-known issue in information systems. Users are faced with the problem of choosing among many possible resources to determine those likely to satisfy their needs. In many cases, users may be assisted in overcoming the information overload problem by being exposed to relevant data items, information, services, etc. In web environments, such as commercial websites, items of interest are determined and recommended to enable more efficient selection of information. Usually, web sites recommend items such as music, articles, web pages, keyword queries, books, online videos, friends, etc. Recommender systems aim at providing recommendations of relevant data to users to improve and accelerate navigation in a vast space of information resources. A recommender system (RS) would explore items to determine those of interest for a particular user based on the user's preferences and interests. Typically, items similar to those a user preferred in the past are recommended as relevant (e.g., content-based approach) and/or items that users with similar tastes and preferences liked in the past (e.g., collaborative approach).
RS could be employed to recommend relevant resources to business users to improve their productivity and help them explore relevant available resources. Conventionally, business users interact with different communicating business applications and systems, e.g., Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Product Lifecycle Management (PLM), etc. Consequently, the recommendation of relevant resources to a business user imposes additional constraints, as opposed to scenarios where a user is limited to a web site within a browser. An example of such constraints is accessing different resources such as documents, processes, applications, structured data, etc., retrieved from various source systems, e.g., CRM, ERP, Human Resources (HR), and Business Intelligence (BI) systems, etc. Usually, such systems impose additional security controls, e.g., restricted access rights to data pertinent to a finance or human resources departments. On contrary, various web resources, e.g., commercial products, video clips, songs, may be freely available and recommended to every user in a web site. Additionally, when a user is interacting with diverse applications, a uniform user profile is rarely shared between the applications. As a result, fragmented or inconsistent data about the user activities and preferences are generated. In many cases, there is a need to recommend heterogeneous resources provided by various computer systems or applications that impose different security controls to a business user.